“We Did It!” Proud Moments as a Catalyst for Engineers’ Situated Leadership Learning
Bibliographic record
Abstract
Engineers' day-to-day responsibilities include supervision, influence, management, and leadership, yet much of this work occurs on the periphery of their professional attention.Our study aims to make the largely implicit process of engineering leadership (EL) development explicit, and thus teachable, by pairing memorable career events with leadership learning processes.More specifically, we use Lave and Wenger's situated learning theory to investigate how career-embedded proud moments contribute to engineers' leadership development.Our team identified four types of proud moments along with corresponding leadership lessons in the career history narratives of 29 senior engineers.This four-part proud moment typology-honing professional dexterity, mobilizing teams, realizing values, and driving excellence-illustrates four distinct ways that engineers can and do institutionalize leadership in their respective workplaces.This finding suggests that proud moments are not only personally affirming stories, but also institutionally realized leadership catalysts.By making four types of EL development catalysts explicit, we provide engineering educators with authentic, industry-embedded narratives to support their programing.This project is significant to the ASEE LEAD division because it provides us with a way of scaffolding leadership development opportunities for all our students, even those who may resist the notion of engineering as a leadership profession.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".